The influence of inter- and intra-channel architecture on deep-water turbidite reservoir performance
Bibliographic record
Abstract
Bed-scale heterogeneity in channelized deep-water reservoirs can significantly influence reservoir performance, but reservoir simulation typically requires cell sizes much greater than the scale of intra-channel element architecture. Here, bed- to geobody-scale simulations elucidate the influence of bed-scale architecture and channel element stacking on flow and connectivity, informing full-field reservoir model development and evaluation. Models consist of two channel element segments, each 300 m (985 ft) wide by 14 m (45 ft) thick and 550 m (1805 ft) long, stacked in 12 different stacking arrangements. Bed-scale architecture is captured in six deterministic element fills, highlighting interbedded sandstone and mudstone (thin bed) presence (homogeneous v. heterogeneous elements), position (symmetrical v. asymmetrical), and proportion (low v. high element net-to-gross). Each model is flow simulated to illuminate how element stacking and intra-element heterogeneity impacts reservoir performance. Thin bed presence and position have the greatest impact on reservoir connectivity/performance when elements are laterally offset; impacts are minimal when elements are vertically aligned. Impacts are exacerbated when the thin-bed proportion is increased. Where bed-scale architecture is represented, complex flow behaviours generate a significant variability in production timing and the cumulative volumes produced. Simulations consisting of a homogenous element architecture fail to capture complex flow behaviours, producing comparatively optimistic results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".